---
title: "La Recherche publique en France"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: journal
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(ggtext)
library(ggpubr)
library(sf)

#IMPORTATION DU JEU DE DONNEES
rdFR <- read.csv("./rd-moyens-administrations.csv", sep = ";")

#AS FACTOR
for (i in 1:12){
  rdFR[,i] <- as.factor(rdFR[,i])
}

#Fond de carte France par region 2014
reg<-st_read(dsn="regions-20140306-100m-shp/regions-20140306-100m.shp")

reg$nom
#On enl?ve les DOM TOM
guadeloupe<-reg[11,]
guyane<-reg[12,]
lareunion<-reg[16,]
martinique<-reg[19,]
mayotte<-reg[20,]

reg<-reg[-c(11:12,16,19:20),]
```

Vision globale
=======================================================================

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r}

```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}

```

### Chart C

```{r}

```

La parité dans la recherche
=======================================================================
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A

```{r}
par1 <- rdFR %>%
  filter(code_indicateur=="pers") %>% 
  filter(sexe!="Non ventile") %>% 
  filter(annee==2001) %>% 
  group_by(code_region,region,annee,sexe) %>% 
  summarise(personnel=sum(valeur)) %>% 
  group_by(region) %>% 
  mutate(percentage=personnel/sum(personnel)*100) %>% 
  filter(sexe=="Femmes")


#On merge les donn?es et le fond de carte
par11<-merge(x=reg,y=par1,by.x="code_insee",by.y="code_region",all.x=T)

par2001 <- par11 %>%
  st_transform( crs = 32631 )%>% 
  ggplot()+
  geom_sf(aes(fill = as.numeric(percentage))) +
  scale_fill_gradient(low = "#F1D9F7", high = "#FA28FA", breaks=c(30,35,40,45,49)) +
  labs(fill = 'Percentage',title=2001)+
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank())+
  theme(plot.title = element_text(hjust = 0.5,size=10))

par2 <- rdFR %>%
  filter(code_indicateur=="pers") %>% 
  filter(sexe!="Non ventile") %>% 
  filter(annee==2013) %>% 
  group_by(code_region,region,annee,sexe) %>% 
  summarise(personnel=sum(valeur)) %>% 
  group_by(region) %>% 
  mutate(percentage=personnel/sum(personnel)*100) %>% 
  filter(sexe=="Femmes")


#On merge les donn?es et le fond de carte
par12<-merge(x=reg,y=par2,by.x="code_insee",by.y="code_region",all.x=T)

par2013 <- par12 %>%
  st_transform( crs = 32631 )%>% 
  ggplot()+
  geom_sf(aes(fill = as.numeric(percentage))) +
  scale_fill_gradient(low = "#F1D9F7", high = "#FA28FA", breaks=c(30,35,40,45,49))+
  labs(fill = 'Percentage',title=2013)+
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank())+
  theme(plot.title = element_text(hjust = 0.5,size=10))

vision01_13 <- ggpubr::ggarrange(
  par2001,
  par2013,
  nrow = 1,
  ncol = 2,
  common.legend = TRUE,
  legend = "bottom"
)

annotate_figure(vision01_13,
                top = text_grob("Proportion of women in public research", color = "black", face = "bold", size = 14),
                bottom = text_grob("Data source: data.gouv",
                                   hjust = 1, x = 1, face = "italic", size = 10)
)
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}

```

### Chart C

```{r}

```


Les institutions
=======================================================================
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A

```{r}

```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}

```

### Chart C

```{r}

```